Results 31 to 40 of about 152,186 (174)
Differentially Private Trajectory Analysis for Points-of-Interest Recommendation [PDF]
, 2017 Ubiquitous deployment of low-cost mobile positioning devices and the widespread use of high-speed wireless networks enable massive collection of large-scale trajectory data of individuals moving on road networks.
Joshi, J, Li, C, Palanisamy, B
core +1 more source
Trajectory differential privacy protection mechanism based on prediction and sliding window
Tongxin xuebao, 2020 To address the issues of privacy budget and quality of service in trajectory differential privacy protection,a trajectory differential privacy mechanism integrating prediction disturbance was proposed.Firstly,Markov chain and exponential perturbation ...
Ayong YE +4 more
doaj +2 more sources
Differentially private count queries over personalized-location trajectory databases
Data in Brief, 2018 Differential privacy is a technique for releasing statistical information about a database without revealing information about its individual data records.
Fatemeh Deldar, Mahdi Abadi
doaj +1 more source
A Trajectory Privacy Protection Method Based on Random Sampling Differential Privacy
ISPRS International Journal of Geo-Information, 2021 With the popularity of location-aware devices (e.g., smart phones), a large number of trajectory data were collected. The trajectory dataset can be used in many fields including traffic monitoring, market analysis, city management, etc.
Tinghuai Ma, Fagen Song
doaj +1 more source
Privacy Protection Algorithm Based on Optimized Local Suppression for Trajectory Data Publication [PDF]
Jisuanji gongcheng, 2020 To address the problem of privacy leakage caused by trajectory sequences in trajectory data publication,this paper proposes a privacy protection algorithm,TPL-Local,based on optimized local suppression.The algorithm identifies the minimal violating ...
YU Qingying, WANG Yanfei, YE Zitong, ZHANG Shuanggui, CHEN Chuanming
doaj +1 more source
A Privacy Risk Model for Trajectory Data [PDF]
, 2014 Time sequence data relating to users, such as medical histories and mobility data, are good candidates for data mining, but often contain highly sensitive information. Different methods in privacypreserving data publishing are utilised to release such private data so that individual records in the released data cannot be re-linked to specific users ...
Basu A +8 more
openaire +3 more sources
EXPHLOT: EXplainable Privacy Assessment for Human LOcation Trajectories
, 2023 AbstractHuman mobility data play a crucial role in understanding mobility patterns and developing analytical services across various domains such as urban planning, transportation, and public health. However, due to the sensitive nature of this data, accurately identifying privacy risks is essential before deciding to release it to the public.
Francesca Naretto +3 more
openaire +6 more sources
LBS user location privacy protection scheme based on trajectory similarity
Scientific Reports, 2022 During the data set input or output, or the data set itself adds noise to enable data distortion to effectively reduce the risk of user privacy leakage.
Kun Qian, Xiaohui Li
doaj +1 more source
Tongxin xuebao, 2021 To solve the problem that most studies had not fully considered the sensitivity of location to privacy budget and the influence of trajectory shape, which made the usability of published trajectory poor, a shape similarity differential privacy trajectory
Suxia ZHU, Shulun LIU, Guanglu SUN
doaj +2 more sources
Time Distortion Anonymization for the Publication of Mobility Data with High Utility [PDF]
, 2015 An increasing amount of mobility data is being collected every day by different means, such as mobile applications or crowd-sensing campaigns. This data is sometimes published after the application of simple anonymization techniques (e.g., putting an ...
Brunie, Lionel +3 more
core +3 more sources